Complexity of single machine scheduling subject to nonnegative inventory constraints

This paper focuses on single machine scheduling subject to inventory constraints. Jobs either add items to an inventory or remove items from that inventory. Jobs that have to remove items cannot be processed if the required number of items is not available. We consider scheduling problems on a singl...

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Veröffentlicht in:European journal of operational research Jg. 207; H. 2; S. 605 - 619
Hauptverfasser: Briskorn, Dirk, Choi, Byung-Cheon, Lee, Kangbok, Leung, Joseph, Pinedo, Michael
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Amsterdam Elsevier B.V 01.12.2010
Elsevier
Elsevier Sequoia S.A
Schriftenreihe:European Journal of Operational Research
Schlagworte:
Applied sciences
Completion time
Complexity
Complexity theory
Computation
Computational complexity
Exact sciences and technology
Inventories
Inventory constraints
Inventory control, production control. Distribution
Inventory management
Job shops
Lateness
Machine scheduling
Machine scheduling Inventory constraints Computational complexity Strong NP-hardness Polynomial-time algorithms
Operational research
Operational research and scientific management
Operational research. Management science
Optimization
Optimization algorithms
Polynomial-time algorithms
Production scheduling
Scheduling
Scheduling, sequencing
Stockpiling
Strong NP-hardness
Studies
Inventory constraints
Polynomial-time algorithms
Machine scheduling
Computational complexity
Strong NP-hardness
Minimization
Scheduling
Makespan
Polynomial method
Delay
Polynomial time
Production management
NP hard problem
Lateness
Single machine
ISSN:0377-2217, 1872-6860
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Zusammenfassung:This paper focuses on single machine scheduling subject to inventory constraints. Jobs either add items to an inventory or remove items from that inventory. Jobs that have to remove items cannot be processed if the required number of items is not available. We consider scheduling problems on a single machine with the minimization of the total weighted completion time, the maximum lateness, and the number of tardy jobs, respectively, as objective and determine their computational complexity. Since the general versions of our problems turn out to be strongly NP-hard, we consider special cases by assuming that different jobs have certain parameter values in common. We determine the computational complexity for all special cases when the objective is either to minimize total completion time or to minimize maximum lateness and for several special cases when the objective is either to minimize total weighted completion time or to minimize the number of tardy jobs.
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ISSN:0377-2217
1872-6860
DOI:10.1016/j.ejor.2010.05.036